Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques

By Nasser H. Dardas, Nicolas D. Georganas. IEEE Transactions on Instrumentation and Measurement, 2011

469 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/0504c46c80aba498434a81c8133c23cc458d42d3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Terrance E. Boult
2. Luc Van Gool
3. Walter J. Scheirer
4. Dacheng Tao
5. Xiaogang Wang
6. Yi Yang
7. Andrew Zisserman
8. Bernt Schiele

In Human-Computer Interaction

1. Shuai Zhou
2. Jun Wan
3. Xiaoyuan Guo
4. Jiali Duan
5. S. Li
6. Zaid Bin Omar
7. J. Laviola
8. Mohamed Hisham Jaward

Citations in the corpus

• Hand gesture recognition with 3D convolutional neural networks (2015, methodology): “As gesture classifiers, hidden Markov models [20], conditional random fields [24] and support vector machines (SVM) [4] have been widely used.”
• A review of hand gesture and sign language recognition techniques (2017, methodology): “Using a similar method as [15], recognition of four gestures are performed with an average accuracy of 90% [66].”
• A Unified Framework for Multi-Modal Isolated Gesture Recognition (2018, methodology): “Manyhand-crafted features have been proposed for video analysis in the area of gesture recognition [Starner et al. 1998; Wang et al. 2006; Dardas and Georganas 2011; Trindade et al. 2012; Wan et al. 2013].”
• Multi-Modality Fusion based on Consensus-Voting and 3D Convolution for Isolated Gesture Recognition (2016, methodology): “Many hand-crafted features have been proposed for video analysis in the area of gesture recognition [28, 39, 2, 31, 35].”
• One-shot learning gesture recognition from RGB-D data using bag of features (2013, unknown): “Dardas and Georganas (2011) proposed a method for real-time hand gesture recognition based on standard BoF model, but they first needed to detect and track hands and that would be difficult in a clutter background.”
• Multi-class Open Set Recognition Using Probability of Inclusion (2014, background): “Emerging research that moves beyond typical binary models of positive/negative class association for open set recognition has examined the issues of detecting novel classes [16,5,4], rejecting outlier or unknown classes [24,57,2], and/or simultaneously detecting and recognizing known classes in the ”
• 3D Gestural Interaction: The State of the Field (2013, background): “Dardas and Georganas [92] SVM with bag of words 10 96.”
• Superpixel-Based Hand Gesture Recognition With Kinect Depth Camera (2015, background): “Once the hands have been located, hand gesture recognition aims to interpret predetermined gestures into independent sign, mostly static, which can be classified by pattern classifiers such as k-Nearest Neighbors (kNN) [8], Hidden Markov Models [9], Principal Component Analysis (PCA) [10] and Suppor”